Detection of genotyping errors by Hardy Weinberg equilibrium testing

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1 (2004) 12, & 2004 Nature Publishing Group All rights reserved /04 $ ARTICLE Detection of genotyping errors by Hardy Weinberg equilibrium testing Louise Hosking*,1, Sheena Lumsden 1, Karen Lewis 1, Astrid Yeo 2, Linda McCarthy 1, Aruna Bansal 2, John Riley 1, Ian Purvis 1 and Chun-Fang Xu 1 1 GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage, Hertfordshire SG1 2NY, UK; 2 GlaxoSmithKline Medicines Research Centre, New Frontiers Science Park, Third Avenue, Harlow, Essex CM19 5AW, UK data sets may contain errors that, in some instances, lead to false conclusions. Deviation from Hardy Weinberg equilibrium (HWE) in random samples may be indicative of problematic assays. This study has analysed genotypes generated by TaqMan, RFLP, sequencing or mass spectrometric methods from 443 single-nucleotide polymorphisms (). These are distributed both within genes and in intergenic regions. Genotype distributions for 36 out of 313 assays (11.5%) whose minor allele frequencies were deviated from HWE (Po0.05). Some of the possible reasons for this deviation were explored: assays for five proved nonspecific, and genotyping errors were identified in 21. For the remaining 10, no reasons for deviation from HWE were identified. We demonstrate the successful identification of a proportion of nonspecific assays, and assays harbouring genotyping error. Consequently, our current high-throughput genotyping system incorporates tests for both assay specificity and deviation from HWE, to minimise the genotype error rate and therefore improve data quality. (2004) 12, doi: /sj.ejhg Published online 11 February 2004 Keywords: Hardy Weinberg equilibrium; genotyping; error Introduction As a consequence of the generation of large numbers of genotypes in both family- and population-based genetic studies, much work is currently focusing on the identification and possible integration of error within such data. Error can be introduced into genetic data sets from a variety of sources, which include inconsistencies within family pedigrees, 1,2 sample mishandling, 3 and errors introduced by the genotyping process itself. 4 Inclusion of incorrect data in genetic analysis can lead to the generation of false conclusions 5,6 and a reduction of power to fine map trait loci, 7,8 and is a recognised problem in the statistical analysis of genetic data sets Previous *Correspondence: Dr L Hosking, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Rd, Stevenage, Hertfordshire SG1 2NY, UK. Tel: þ ; Fax: þ ; louise.k.hosking@gsk.com Received 28 August 2003; revised 27 November 2003; accepted 9 December 2003 simulation studies have considered the impact of genotyping errors in data sets generated from pedigrees; 5,10 even genotyping error rates as low as 1 2% can affect both linkage and sib-pair studies. 5 Within family studies, incorrect genotypes may inflate map distance between 7,14 16 markers and also reduce the power to detect linkage, and contribute to an inflated false positive rate among transmission disequilibrium test (TDT)-derived associations. 17 Within family studies, a proportion of genotyping errors can be detected by incorporating checks for consistency with Mendelian inheritance. 2,9,18 Checking for Mendelian inconsistency will not, however, exclude all genotyping errors, and cannot be applied to population-based studies. The presence of errors within genotyping data sets generated from unrelated individuals has considerable impact on subsequent data analysis, as no checks for Mendelian consistency are possible within such data sets. 19 It has also been demonstrated in a simulation study that

2 396 genotyping error rates as low as 3% can adversely affect linkage disequilibrium (LD) measures. 20 This could limit attempts to identify complex disease genes, because it has been demonstrated that genotyping errors always decrease the power of certain statistical tests for linkage and/or association. For example, the w 2 test of independence applied to case:control data always loses power in the presence of genotyping errors. 8,21,22 Statistical tools that are able to take error into account have been developed. The majority of these models are applicable to linkage studies or TDTs. 17,27 error within data sets generated from unrelated individuals is also currently being addressed within certain statistical models. 19,28 This study used the Hardy Weinberg equilibrium (HWE) test to identify genotyping error within population-based data sets. In large enough randomly mating populations, not subject to genetic and population parameters affecting allele frequencies, the genotypes for an individual marker should distribute according to the principle of HWE. 29 Technical reasons, such as assay nonspecificity and genotyping errors, can also impact on the distribution of genotypes for any one marker. These technical reasons for distribution deviation were explored in this study. As a result, an improved genotyping process was implemented to reduce the occurrence of genotype errors. Subjects and methods DNA samples A total of 2750 Caucasian samples have been employed in this analysis. A total of 2008 of these samples have already been reported elsewhere. In addition, 588 samples were collated from North European Caucasians within GlaxoSmithKline with consent for nonidentified genotyping. In all, 92 Caucasian DNA samples were collected from North America with informed consent for nonidentified genotyping, and 62 Caucasian DNA samples were purchased from Coriell cell repositories (Camden, New Jersey, USA) (Table 1). A total of 443 single-nucleotide polymorphisms () were typed using different sets of the DNA samples and Table 1 and samples used for each of the four technologies methodology samples Taqman Maldi-tof RFLP Sequencing different methodologies (Table 1) generating genotypes. Determination of deviation of genotype distribution from HWE Minor allele frequencies were recorded for all of the 443 (Table 2). A subset of 313, whose minor allele frequencies were 45%, was analysed for deviation of genotype distribution from HWE, using the w 2 statistic with one degree of freedom. whose genotype distribution deviated from HWE (Po0.05) were identified (Table 3), and are also referred to as HWD. Assay specificity Nucleotide homology searches were performed on the primer and probe sequences defining reaction specificity for each of the deviating from HWE (Po0.05), using the NRNUC and NRHTG (Human Genome from EMBL, Sanger Centre and Washington University) databases in March Results In all, genotypes were generated from 443, with minor allele frequencies ranging between and 0.49 (Table 2). Of the 443, 81% (353/443) were distributed throughout the genome. Of the remaining 90, 38 mapped to a 400 kb, region on chromosome 22, to a region on chromosome 19, 30 and 28 to a region on chromosome A subset of 313, whose minor allele frequencies were 45%, was selected for the estimation of deviation from HWE. A total of 36 (11.5%), with minor allele frequencies ranging between 0.06 and 0.49, were found to deviate from HWE (Po0.05) (Table 3). Of the 36 demonstrating HWD, 20 displayed deviation from HWE at the Po0.01 level (Table 3). Controlling the false discovery rate, of them would be considered significant at the 5% level. Possible explanations for that showed deviation from HWE were explored. An SNP assay was classified as nonspecific if a primer and/or probe set showed 100% homology with multiple regions in the genome. Five of the 36 (14%) were found to have nonspecific assays. Table 2 Distribution of minor allele frequencies of 443 Minor allele frequency %

3 Table 3 SNP ID (36) exhibiting HWD (Po0.05) Minor allele frequency samples technology 1 a,b Maldi-tof 2 a,b Maldi-tof 3 a,b Maldi-tof 4 a,b Maldi-tof 5 a,b RFLP 6 a,b Taqman 7 a,b Taqman Taqman Taqman Taqman 11 a,b Taqman Taqman 13 a Taqman 14 a Sequencing 15 a Sequencing 16 a,b Sequencing 17 a,b Sequencing Sequencing Sequencing Sequencing 21 a,b Sequencing Sequencing Sequencing Sequencing 25 a,b Sequencing 26 a,b Sequencing 27 a,b Sequencing Sequencing Sequencing Sequencing Sequencing 32 a Sequencing 33 a,b Sequencing Sequencing 35 a,b Sequencing Sequencing a Po0.01. b Po0.05 controlling the false discovery rate. errors were identified in 21 assays, accounting for 58% of the showing deviation from HWE. For the remaining 10 (28%), no reasons for the observed HWD were identified. When analysing assays that deviated from HWE at the Po0.01 level (Table 4), the percentage of associated with genotyping errors was slightly lower, and the proportion associated with nonspecificity slightly higher, than all HWD (Po0.05) assays. For the 21 assays where deviation of genotype distribution from HWE (Po0.05) was due to genotyping error (Table 4), the data sets were stratified according to each genotyping technology used (Table 5), and according to the level of deviation from HWE (0.01oPo0.05, or Po0.01). Sources of error appeared to be dependent, at least in part, on the methodology used to type the SNP. One type of error seen in analysed by directly sequencing PCR products was the inability to distinguish accurately genotypes if the background signal was too high on the sequence trace. An error frequently associated with data generated using Taqman methodology was the Table 4 Identifiable reasons for deviation from HWE in 36 Deviation from HWE Reason for deviation % Po0.05 error Nonspecific 5 14 Unknown Total 36 Po0.01 error 9 45 Nonspecific 4 20 Unknown 7 35 Total 20 inaccurate calling of individual genotypes if those individual genotypes fell between the three main genotype clusters. In general, the proportion of assays associated with genotyping error is slightly lower (2.9%) in the Po0.01 assays than in the 0.01o Po0.05 assays (3.8%), although following this stratification (Table 5), the numbers of assays studied are low. A greater proportion of RFLP assays appear to harbour genotyping error, but the number of assays studied (10) was low. Discussion Generation and analysis of large SNP genotyping data sets for the investigation of human complex disorders is currently the subject of much discussion, and focus for activity. 34 Large genotyping data sets will inevitably contain some error, which has long been recognised as a problem in the accurate statistical analysis of genetic data. 1,9 As genotyping errors are known specifically to affect certain genetic measurements such as LD, upon which association studies depend, 20 and also to affect family-based studies of linkage and association, 17 the identification of error is critical to accurate analysis and subsequent interpretation of the data. Current interest also surrounds the development of statistical methods that are able to take error into account in genetic data analysis. 17,19,23 26 This large, empirical study reports the measurement of genotype distribution deviation from HWE as a method to identify and reduce genotyping errors generated as a result of the genotyping process itself in population-based studies. The study measured deviation from HWE in 313 and revealed 36 HWD (Po0.05) assays, which is B2.3 more than expected by chance. When considering these data, it is important to remember that the sensitivity of measurement of deviation from HWE will also depend on the minor allele frequencies of the typed ( ), and the number of samples analysed ( ). Further investigation of the 36 HWD (Po0.05) assays revealed that 58% of them harboured genotyping error. 397

4 398 Table 5 Stratification of 21 HWD (Po0.05) assays due to genotyping error by methodology Deviation from HWE Methodology genotype data sets generated by each methodology data sets associated with genotyping error % 0.01oPo0.05 Taqman Sequencing Maldi-tof RFLP Total Po0.01 Taqman Sequencing Maldi-tof RFLP Total In this study, when the primers defining assay specificity were designed, high-level repeats in the genome sequence, including Alus and LINE, were masked. However, low-level repeat sequences are more difficult to monitor. In order to address this, primers and/or probes defining the reaction specificity for each of the 36 assays in HWD (Po0.05) were retrospectively analysed, by searching against NRNUC and NRHTG databases. In order to identify possible sequence homologies, no sequence filters were used at this stage. Assays developed for five appeared to be nonspecific. Two of these were that mapped to a 390 kb region on chromosome 22 flanking CYP2D6. 32 Two pseudogenes, CYP2D7 and CYP2D8, lie adjacent to CYP2D6 and the primers defining assay specificity for the two nonspecific were found to map to either or both of the pseudogenes. Pseudogenes are clearly abundant, 35 and therefore experimental design must take these sequences into consideration. The other three nonspecific SNP assays demonstrated 100% homology to more than one chromosomal region. After analysis of the 36 HWD (Po0.05), and the identification of those assays associated with genotyping error or nonspecificity, 10 remained. It is possible that the deviation from HWE observed in these is occurring by chance. However, as a large proportion of lowlevel duplications have not been represented following the assembly of the draft human genome sequence, 36 it is conceivable that within this group of HWD (Po0.01) there are some assays that may be nonspecific, but all the sequences that their probes and primers are homologous to are not captured in the human genome sequence assembly. The data reported here were generated during using various different genotyping technologies. As a result of this retrospective analysis, a high-throughput standardised semi-automated genotyping process has been developed. This incorporates automatic electronic PCR of primers before running the assay. All SNP assays are tested for deviation from HWE in 94 Caucasian DNA samples prior to running the SNP assay across the DNA sample set of interest. Genotypes are scored by highly trained scientists, and data accuracy is not compromised for individual assay genotype success rates. Following this process, analysis of genotypes generated from 94 unrelated Caucasians for 1434 revealed only 10 HWD (Po0.01) (unpublished data). This is slightly less than the number expected to occur by chance (B14), suggesting improved data quality. In conclusion, this study demonstrates the successful identification of a proportion of nonspecific assays and assays harbouring genotyping errors, by using a simple test for HWE. The genotyping process was subsequently modified in order to generate data of improved quality. Acknowledgements We are grateful to all the scientists within Discovery Genetics Europe, GlaxoSmithKline for sharing their data with us. In particular, we thank Nicola White, Jennifer O Sullivan, and Christine Caine. We also thank scientists within Population Genetics, GlaxoSmithKline, particularly Peter Boyd. References 1 Douglas JA, Boehnke M, Lange K: A multipoint method for detecting genotyping errors and mutations in sibling-pair linkage data. Am J Hum Genet 2000; 66: Gordon D, Heath SC, Ott J: True pedigree errors more frequent than apparent errors for single nucleotide polymorphisms. Hum Hered 1999; 49: Ewen KR, Bahlo M, Treloar SA et al: Identification and analysis of error types in high-throughput genotyping. Am J Hum Genet 2000; 67: Sobel E, Papp JC, Lange K: Detection and integration of genotyping errors in statistical genetics. Am J Hum Genet 2002; 70: Abecasis GR, Cherny SS, Cardon L: The impact of genotyping error on family based analysis of quantitative traits. Eur J Hum Genet 2001; 9:

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